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    Area of Science:

    • Control Theory
    • Artificial Intelligence
    • Systems Engineering

    Background:

    • Optimal consensus control for discrete linear multiagent systems is challenging with partially observable states.
    • Traditional methods rely on distributed observers, which are often infeasible due to difficulties in obtaining accurate system models.
    • Analytical solutions for optimal control policies are unobtainable without complete system functions.

    Purpose of the Study:

    • To propose a novel data-driven adaptive dynamic programming approach for optimal output consensus control.
    • To overcome the limitations of traditional observer-based methods in systems with unobservable states and unknown dynamics.
    • To develop a method that does not require complete system state information or analytical system functions.

    Main Methods:

    • Utilizing input and output sequences as an equivalent representation of the system's underlying state.
    • Developing an adaptive dynamic programming algorithm to generate optimal control policies based on this representation.
    • Employing a neural network-based actor-critic structure to approximate local performance indices and control policies.

    Main Results:

    • The proposed data-driven approach effectively generates optimal control policies without needing full system state information.
    • The neural network-based actor-critic implementation successfully approximates performance indices and control policies.
    • Two numerical simulations validated the method's effectiveness in addressing optimal output consensus control problems.

    Conclusions:

    • The data-driven adaptive dynamic programming approach offers a viable solution for optimal consensus control in multiagent systems with partially observable states.
    • This method eliminates the need for explicit system models and distributed observers, enhancing practical applicability.
    • The use of neural networks provides an effective mechanism for policy and value function approximation in complex control scenarios.